arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large language models (LLMs) as intelligent automated evaluation assistants. Although recent studies show that LLMs can generate fluent critiques and approximate reviewer scores, their reliability, robustness, and security as decision-support systems remain insufficiently understood. This survey offers a systems-level analysis of LLM-based scientific peer review, focusing on two core evaluative functions: crit
The rapid growth of scientific submissions and advances in LLM capabilities are pushing the need for automated peer review solutions.
The introduction of LLM-based peer review could significantly alter academic publishing workflows, peer review quality, and the speed of scientific dissemination.
LLMs are moving from theoretical applications to practical, albeit challenged, roles in critical academic processes, impacting efficiency and potentially objectivity.
- · Academic publishers
- · Researchers with fast publication needs
- · AI model developers
- · Startups offering academic tooling
- · Traditional human peer reviewers
- · Journals with slow review processes
- · Authors submitting marginal research
LLMs begin to augment or replace human reviewers for initial screening and feedback generation.
The quality and ethical standards for LLM-based academic review systems become a major point of research and development, potentially leading to new regulatory frameworks.
The speed of scientific discovery and dissemination accelerates dramatically, leading to more frequent, albeit potentially less thoroughly vetted, publications.
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Read at arXiv cs.CL